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Background: Early identification of language disorders ensures children in need receive support to improve development and quality of life outcomes. Two previous studies identified six parent-reported survey items that predicted a comparable 11-year language outcome with ≥71% sensitivity and specificity. One used data from the Early Language in Victoria Study (ELVS) and the other from the Longitudinal Study of Australian Children (LSAC) birth cohort. This study will combine data from these two cohorts and add polygenic scores (PGS) of 24-38-month vocabulary. We will assess whether predictive accuracy of 11-year language outcome improves when adding PGS to a set of parent-reported behavioral predictors. Method: ELVS and LSAC collected six parent-reported measures about the child’s development between 24-36 months of age and a language outcome at 11 years. Both studies also collected genetic data at 7 (ELVS) or 11-12 (LSAC) years of age from which we derived PGS of 24-38-month vocabulary. We will use an ensemble machine-learning approach (SuperLearner) to estimate the accuracy of the six survey items and the PGS in predicting low 11-year language outcome (>1.5SD below the mean). Expected results: Predictive accuracy may improve when combining the parent-reported survey items with PGS. This will inform the extent to which future studies should prioritize collecting genetic data to help predict language outcomes.

Language provides a foundation for quality of life and development, but language difficulties, affecting 10% of children, are generally only identified late in childhood. This study aims to identify a parsimonious set of preschool indicators that predict language outcomes, using data from the Early Language in Victoria Study (n=851). Between 8 months and 3 years, parents completed surveys relating to their child and at 11 years the children completed a standardised language assessment. This study uses random forests and SuperLearner machine learning methods to identify which parent-reported questions best predict children’s language skills at 11 years. We hypothesise that gestures, vocabulary, morphosyntax, family history of Language Disorder, socioeconomic status and parent-child interactions predict language outcomes. A short, accurate set of predictors of language outcomes would allow targeted recruitment of participants at risk of persisting language difficulties into intervention studies and could be translated into a screening tool for Language Disorder.

The magnocellular-dorsal system is well isolated by high temporal frequency. However, temporal processing thresholds have seldom been explored in developmental dyslexia nor its subtypes. Hence, performances on two, four-alternative forced-choice achromatic flicker fusion threshold tasks modulated at low (5%) and high (75%) temporal contrast were compared in dyslexic and neurotypical children individually matched for age and intelligence (8-12 years, n = 54 per group). As expected, the higher modulation resulted in higher flicker fusion thresholds in both groups. Compared to neurotypicals, the dyslexic group displayed significantly lower ability to detect flicker at high temporal frequencies, both at low and high temporal contrast. Yet, discriminant analysis did not adequately distinguish the dyslexics from neurotypicals, on the basis of flicker thresholds alone. Rather, two distinct dyslexic subgroups were identified by cluster analysis - one characterised by significantly lower temporal frequency thresholds than neurotypicals (referred to as 'Magnocellular-Deficit' dyslexics; 53.7%), while the other group ('Magnocellular-Typical' dyslexics; 46.3%) had comparable thresholds to neurotypicals. The two dyslexic subgroups were not differentially associated with phonological or naming speed subtypes and showed comparable mean reading rate impairments. However, correlations between low modulation flicker fusion threshold and reading rate for the two subgroups were significantly different (p = .0009). Flicker fusion threshold performances also showed strong classification accuracy (79.3%) in dissociating the Magnocellular-Deficit dyslexics and neurotypicals. We propose that temporal visual processing impairments characterize a previously unidentified subgroup of dyslexia and suggest that measurement of flicker fusion thresholds could be used clinically to assist early diagnosis and appropriate treatment recommendations for dyslexia.

Dynamic visual attention training using Action Video Games (AVGs) is a promising intervention for dyslexia. This study investigated the efficacy of 5 h (10 × 30 min) of AVG training in dyslexic children (aged 8-13) using 'Fruit Ninja', while exploring whether increasing attentional and eye movement demands enhanced AVG effectiveness. Regular (AVG-R; n = 22) and enhanced AVG training (AVG+; n = 23) were compared to a treatment-as-usual comparison group (n = 19) on reading, rapid naming, eye movements and visuo-temporal processing. Playing 'Fruit Ninja' for only 5 h significantly improved reading accuracy, rate, comprehension and rapid naming of both AVG groups, compared to the comparison group, though increasing attentional demands did not enhance AVG efficacy. Participants whose low contrast magnocellular-temporal processing improved most following training also showed significantly greater improvement in reading accuracy. The findings demonstrate a clear role for visual attention in reading and highlight the clinical applicability of AVGs as a fun, motivational and engaging intervention for dyslexia.

Grammatical categories encode conceptual domains and contrast them with others. They include the part-of-speech categories of words as well as morphological markers that encode functions like number, case, gender, tense, aspect or voice. In linguistic terminology, morphology deals with the grammar of words (Booij 2012), whereas syntax is concerned with the relationship between words. Words differ regarding the syntactic functions they can assume (part-of-speech category or word class), as well as their morphological structure: a word stem can be combined with inflectional and derivational affixes, or undergo stem changes to encode grammatical functions; and words can also take part in compositional processes to form new, more complex words (compounding, derivation). Grammatical morphology is typically encoded by affixes that combine with content words like verbs, nouns and adjectives. Typological research shows that languages differ widely regarding the categories they differentiate by means of morphology, and the way in which they do it (cf. Ch. 6). The language learning child has to segment complex morphological forms in order to identify the stem, but also in order to identify the function of the grammatical markers, and establish inflectional paradigms. When the system is fully mastered, the speaker is able to use the lexical items as well as the inflectional markers in the appropriate range of constructions, while also observing constraints in the use of these forms. Section 12.2 provides an introduction to the formal and functional specification of grammatical categories, followed by a characterization on the learning strategies needed to acquire complex grammatical forms (Section 12.3). After a discussion of typical morphological development and the criteria to assess development (Section 12.4), processes in acquiring the form-function mapping in plural formation and tense aspect marking will be discussed (Section 12.5).